OpenRaiser/Intern-Atlas huggingface dataset:
Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces
Venue: arXiv cs.LG 2025
Year: 2025

Objective: SSVEP-based BCI spellers assist individuals experiencing speech difficulties by enabling them to communicate at a fast rate. However, achieving a high information transfer rate (ITR) in most prominent methods requires an extensive calibration period before using the system, leading to discomfort for new users. We address this issue by proposing a novel method that adapts a powerful deep neural network (DNN) pre-trained on data from source domains (data from former users or participants of previous experiments), to the new user (target domain) using only unlabeled target data. Approach: Our method adapts the pre-trained DNN to the new user by minimizing our proposed custom loss function composed of self-adaptation and local-regularity terms. The self-adaptation term uses the pseudo-label strategy, while the novel local-regularity term exploits the data structure and forces the DNN to assign similar labels to adjacent instances. Main results: Our method achieves excellent ITRs of 201.15 bits/min and 145.02 bits/min on the benchmark and BETA datasets, respectively, and outperforms the state-of-the-art alternatives. Our code is available at https://github.com/osmanberke/SFDA-SSVEP-BCI Significance: The proposed method prioritizes user comfort by removing the burden of calibration while maintaining an excellent character identification accuracy and ITR. Because of these attributes, our approach could significantly accelerate the adoption of BCI systems into everyday life.
On the Importance of Backbone to the Adversarial Robustness of Object Detectors
Venue: arXiv cs.CV 2025
Year: 2025

Object detection is a critical component of various security-sensitive applications, such as autonomous driving and video surveillance. However, existing object detectors are vulnerable to adversarial attacks, which poses a significant challenge to their reliability and security. Through experiments, first, we found that existing works on improving the adversarial robustness of object detectors give a false sense of security. Second, we found that adversarially pre-trained backbone networks were essential for enhancing the adversarial robustness of object detectors. We then proposed a simple yet effective recipe for fast adversarial fine-tuning on object detectors with adversarially pre-trained backbones. Without any modifications to the structure of object detectors, our recipe achieved significantly better adversarial robustness than previous works. Finally, we explored the potential of different modern object detector designs for improving adversarial robustness with our recipe and demonstrated interesting findings, which inspired us to design state-of-the-art (SOTA) robust detectors. Our empirical results set a new milestone for adversarially robust object detection. Code and trained checkpoints are available at https://github.com/thu-ml/oddefense.
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU
Venue: arXiv cs.LG 2025
Year: 2025

Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Temporal Convolutional Networks (TCN), Transformer, Kolmogorov-Arnold networks (KAN), Generative Models, Deep Reinforcement Learning (DRL), and Deep Transfer Learning. We examine the structure, applications, benefits, and limitations of each model. Furthermore, we perform an analysis using three publicly available datasets: IMDB, ARAS, and Fruit-360. We compared the performance of six renowned deep learning models: CNN, RNN, Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU alongside two newer models, TCN and Transformer, using the IMDB and ARAS datasets. Additionally, we evaluated the performance of eight CNN-based models, including VGG (Visual Geometry Group), Inception, ResNet (Residual Network), InceptionResNet, Xception (Extreme Inception), MobileNet, DenseNet (Dense Convolutional Network), and NASNet (Neural Architecture Search Network), for image classification tasks using the Fruit-360 dataset.
HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis
Venue: arXiv cs.CL 2025
Year: 2025

As the use of Large Language Models (LLMs) in text generation tasks proliferates, concerns arise over their potential to compromise academic integrity. The education sector currently tussles with distinguishing student-authored homework assignments from AI-generated ones. This paper addresses the challenge by introducing HowkGPT, designed to identify homework assignments generated by AI. HowkGPT is built upon a dataset of academic assignments and accompanying metadata [17] and employs a pretrained LLM to compute perplexity scores for student-authored and ChatGPT-generated responses. These scores then assist in establishing a threshold for discerning the origin of a submitted assignment. Given the specificity and contextual nature of academic work, HowkGPT further refines its analysis by defining category-specific thresholds derived from the metadata, enhancing the precision of the detection. This study emphasizes the critical need for effective strategies to uphold academic integrity amidst the growing influence of LLMs and provides an approach to ensuring fair and accurate grading in educational institutions.
An Alternate Proof of Near-Optimal Light Spanners
Venue: arXiv cs.DS 2025
Year: 2025

In 2016, a breakthrough result of Chechik and Wulff-Nilsen [SODA '16] established that every $n$-node graph $G$ has a $(1+\varepsilon)(2k-1)$-spanner of lightness $O_{\varepsilon}(n^{1/k})$, and recent followup work by Le and Solomon [STOC '23] generalized the proof strategy and improved the dependence on $\varepsilon$. We give a new proof of this result, with the improved $\varepsilon$-dependence. Our proof is a direct analysis of the often-studied greedy spanner, and can be viewed as an extension of the folklore Moore bounds used to analyze spanner sparsity.
Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep Learning
Venue: arXiv cs.CV 2025
Year: 2025

Infrared images captured under turbulent conditions are degraded by complex geometric distortions and blur. We address infrared deturbulence as an image restoration task, proposing DparNet, a parameter-assisted multi-frame network with a wide & deep architecture. DparNet learns a degradation prior (key parameter matrix) directly from degraded images without external knowledge. Its wide & deep architecture uses these learned parameters to directly modulate restoration, achieving spatially and intensity adaptive results. Evaluated on dedicated infrared deturbulence (49,744 images) and visible image denoising (109,536 images) datasets, DparNet significantly outperforms State-of-the-Art (SOTA) methods in restoration performance and efficiency. Notably, leveraging these parameters improves PSNR by 0.6-1.1 dB with less than 2% increase in model parameters and computational complexity. Our work demonstrates that degraded images hide key degradation information that can be learned and utilized to boost adaptive image restoration.
A Global Context Mechanism for Sequence Labeling
Venue: arXiv cs.CL 2025
Year: 2025

Global sentence information is crucial for sequence labeling tasks, where each word in a sentence must be assigned a label. While BiLSTM models are widely used, they often fail to capture sufficient global context for inner words. Previous work has proposed various RNN variants to integrate global sentence information into word representations. However, these approaches suffer from three key limitations: (1) they are slower in both inference and training compared to the original BiLSTM, (2) they cannot effectively supplement global information for transformer-based models, and (3) the high time cost associated with reimplementing and integrating these customized RNNs into existing architectures. In this study, we introduce a simple yet effective mechanism that addresses these limitations. Our approach efficiently supplements global sentence information for both BiLSTM and transformer-based models, with minimal degradation in inference and training speed, and is easily pluggable into current architectures. We demonstrate significant improvements in F1 scores across seven popular benchmarks, including Named Entity Recognition (NER) tasks such as Conll2003, Wnut2017 , and the Chinese named-entity recognition task Weibo, as well as End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) benchmarks such as Laptop14, Restaurant14, Restaurant15, and Restaurant16. With out any extra strategy, we achieve third highest score on weibo NER benchmark. Compared to CRF, one of the most popular frameworks for sequence labeling, our mechanism achieves competitive F1 scores while offering superior inference and training speed. Code is available at: https://github.com/conglei2XU/Global-Context-Mechanism
BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline
Venue: arXiv cs.SI 2025
Year: 2025

Twitter, as one of the most popular social networks, provides a platform for communication and online discourse. Unfortunately, it has also become a target for bots and fake accounts, resulting in the spread of false information and manipulation. This paper introduces a semi-automatic machine learning pipeline (SAMLP) designed to address the challenges associated with machine learning model development. Through this pipeline, we develop a comprehensive bot detection model named BotArtist, based on user profile features. SAMLP leverages nine distinct publicly available datasets to train the BotArtist model. To assess BotArtist's performance against current state-of-the-art solutions, we evaluate 35 existing Twitter bot detection methods, each utilizing a diverse range of features. Our comparative evaluation of BotArtist and these existing methods, conducted across nine public datasets under standardized conditions, reveals that the proposed model outperforms existing solutions by almost 10% in terms of F1-score, achieving an average score of 83.19% and 68.5% over specific and general approaches, respectively. As a result of this research, we provide one of the largest labeled Twitter bot datasets. The dataset contains extracted features combined with BotArtist predictions for 10,929,533 Twitter user profiles, collected via Twitter API during the 2022 Russo-Ukrainian War over a 16-month period. This dataset was created based on [Shevtsov et al., 2022a] where the original authors share anonymized tweets discussing the Russo-Ukrainian war, totaling 127,275,386 tweets. The combination of the existing textual dataset and the provided labeled bot and human profiles will enable future development of more advanced bot detection large language models in the post-Twitter API era.
Lightweight Vision Transformer with Bidirectional Interaction
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in vision backbones have significantly improved their performance by simultaneously modeling images' local and global contexts. However, the bidirectional interaction between these two contexts has not been well explored and exploited, which is important in the human visual system. This paper proposes a Fully Adaptive Self-Attention (FASA) mechanism for vision transformer to model the local and global information as well as the bidirectional interaction between them in context-aware ways. Specifically, FASA employs self-modulated convolutions to adaptively extract local representation while utilizing self-attention in down-sampled space to extract global representation. Subsequently, it conducts a bidirectional adaptation process between local and global representation to model their interaction. In addition, we introduce a fine-grained downsampling strategy to enhance the down-sampled self-attention mechanism for finer-grained global perception capability. Based on FASA, we develop a family of lightweight vision backbones, Fully Adaptive Transformer (FAT) family. Extensive experiments on multiple vision tasks demonstrate that FAT achieves impressive performance. Notably, FAT accomplishes a 77.6% accuracy on ImageNet-1K using only 4.5M parameters and 0.7G FLOPs, which surpasses the most advanced ConvNets and Transformers with similar model size and computational costs. Moreover, our model exhibits faster speed on modern GPU compared to other models. Code will be available at https://github.com/qhfan/FAT.
FreeCHR: An Algebraic Framework for CHR-Embeddings
Venue: arXiv cs.PL 2025
Year: 2025

We introduce the framework FreeCHR which formalizes the embedding of Constraint Handling Rules (CHR) into a host language, using the concept of initial algebra semantics from category theory. We hereby establish a high-level implementation scheme for CHR as well as a common formalization for both theory and practice. We propose a lifting of the syntax of CHR via an endofunctor in the category Set and a lifting of the very abstract operational semantics of CHR into FreeCHR, using the free algebra, generated by the endofunctor. We give proofs for soundness and completeness w.r.t. its original definition. We also propose a first abstract execution algorithm and prove correctness w.r.t. the operational semantics. Finally, we show the practicability of our approach by giving two possible implementations of this algorithm in Haskell and Python. Under consideration in Theory and Practice of Logic Programming (TPLP).
Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features
Venue: arXiv cs.CR 2025
Year: 2025

Advanced cyber threats (e.g., Fileless Malware and Advanced Persistent Threat (APT)) have driven the adoption of provenance-based security solutions. These solutions employ Machine Learning (ML) models for behavioral modeling and critical security tasks such as malware and anomaly detection. However, the opacity of ML-based security models limits their broader adoption, as the lack of transparency in their decision-making processes restricts explainability and verifiability. We tailored our solution towards Graph Neural Network (GNN)-based security solutions since recent studies employ GNNs to comprehensively digest system provenance graphs for security-critical tasks. To enhance the explainability of GNN-based security models, we introduce PROVEXPLAINER, a framework offering instance-level security-aware explanations using an interpretable surrogate model. PROVEXPLAINER's interpretable feature space consists of discriminant subgraph patterns and graph structural features, which can be directly mapped to the system provenance problem space, making the explanations human interpretable. We show how PROVEXPLAINER synergizes with current state-of-the-art (SOTA) GNN explainers to deliver domain and instance-specific explanations. We measure the explanation quality using the Fidelity+/Fidelity- metric as used by traditional GNN explanation literature, we incorporate the precision/recall metric, where we consider the accuracy of the explanation against the ground truth, and we designed a human actionability metric based on graph traversal distance. On real-world Fileless and APT datasets, PROVEXPLAINER achieves up to 29%/27%/25%/1.4x higher Fidelity+, precision, recall, and actionability (where higher values are better), and 12% lower Fidelity- (where lower values are better) when compared against SOTA GNN explainers.
Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

Multi-robot path finding in dynamic environments is a highly challenging classic problem. In the movement process, robots need to avoid collisions with other moving robots while minimizing their travel distance. Previous methods for this problem either continuously replan paths using heuristic search methods to avoid conflicts or choose appropriate collision avoidance strategies based on learning approaches. The former may result in long travel distances due to frequent replanning, while the latter may have low learning efficiency due to low sample exploration and utilization, and causing high training costs for the model. To address these issues, we propose a path planning method, MAPPOHR, which combines heuristic search, empirical rules, and multi-agent reinforcement learning. The method consists of two layers: a real-time planner based on the multi-agent reinforcement learning algorithm, MAPPO, which embeds empirical rules in the action output layer and reward functions, and a heuristic search planner used to create a global guiding path. During movement, the heuristic search planner replans new paths based on the instructions of the real-time planner. We tested our method in 10 different conflict scenarios. The experiments show that the planning performance of MAPPOHR is better than that of existing learning and heuristic methods. Due to the utilization of empirical knowledge and heuristic search, the learning efficiency of MAPPOHR is higher than that of existing learning methods.
Unlearnable Examples Give a False Sense of Data Privacy: Understanding and Relearning
Venue: arXiv cs.CV 2025
Year: 2025

Unlearnable examples are proposed to prevent third parties from exploiting unauthorized data, which generates unlearnable examples by adding imperceptible perturbations to public publishing data. These unlearnable examples proficiently misdirect the model training process, leading it to focus on learning perturbation features while neglecting the semantic features of the image. In this paper, we make an in-depth analysis and observe that models can learn both image features and perturbation features of unlearnable examples at an early training stage, but are rapidly trapped in perturbation features learning since the shallow layers tend to learn on perturbation features and propagate harmful activations to deeper layers. Based on the observations, we propose Progressive Staged Training, a self-adaptive training framework specially designed to break unlearnable examples. The proposed framework effectively prevents models from becoming trapped in learning perturbation features. We evaluated our method on multiple model architectures over diverse datasets, e.g., CIFAR-10, CIFAR-100, and ImageNet-mini. Our method circumvents the unlearnability of all state-of-the-art methods in the literature, revealing that existing unlearnable examples give a false sense of privacy protection and provide a reliable baseline for further evaluation of unlearnable techniques.
MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning
Venue: arXiv cs.CV 2025
Year: 2025

We introduce MoviePuzzle, a novel challenge that targets visual narrative reasoning and holistic movie understanding. Despite the notable progress that has been witnessed in the realm of video understanding, most prior works fail to present tasks and models to address holistic video understanding and the innate visual narrative structures existing in long-form videos. To tackle this quandary, we put forth MoviePuzzle task that amplifies the temporal feature learning and structure learning of video models by reshuffling the shot, frame, and clip layers of movie segments in the presence of video-dialogue information. We start by establishing a carefully refined dataset based on MovieNet by dissecting movies into hierarchical layers and randomly permuting the orders. Besides benchmarking the MoviePuzzle with prior arts on movie understanding, we devise a Hierarchical Contrastive Movie Clustering (HCMC) model that considers the underlying structure and visual semantic orders for movie reordering. Specifically, through a pairwise and contrastive learning approach, we train models to predict the correct order of each layer. This equips them with the knack for deciphering the visual narrative structure of movies and handling the disorder lurking in video data. Experiments show that our approach outperforms existing state-of-the-art methods on the \MoviePuzzle benchmark, underscoring its efficacy.
A Fast Algorithm for Computing Prefix Probabilities
Venue: arXiv cs.FL 2025
Year: 2025

Multiple algorithms are known for efficiently calculating the prefix probability of a string under a probabilistic context-free grammar (PCFG). Good algorithms for the problem have a runtime cubic in the length of the input string. However, some proposed algorithms are suboptimal with respect to the size of the grammar. This paper proposes a novel speed-up of Jelinek and Lafferty's (1991) algorithm, whose original runtime is $O(n^3 |N|^3 + |N|^4)$, where $n$ is the input length and $|N|$ is the number of non-terminals in the grammar. In contrast, our speed-up runs in $O(n^2 |N|^3+n^3|N|^2)$.
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in industrial anomaly detection (AD) have demonstrated that incorporating a small number of anomalous samples during training can significantly enhance accuracy. However, this improvement often comes at the cost of extensive annotation efforts, which are impractical for many real-world applications. In this paper, we introduce a novel framework, Weak}ly-supervised RESidual Transformer (WeakREST), designed to achieve high anomaly detection accuracy while minimizing the reliance on manual annotations. First, we reformulate the pixel-wise anomaly localization task into a block-wise classification problem. Second, we introduce a residual-based feature representation called Positional Fast Anomaly Residuals (PosFAR) which captures anomalous patterns more effectively. To leverage this feature, we adapt the Swin Transformer for enhanced anomaly detection and localization. Additionally, we propose a weak annotation approach, utilizing bounding boxes and image tags to define anomalous regions. This approach establishes a semi-supervised learning context that reduces the dependency on precise pixel-level labels. To further improve the learning process, we develop a novel ResMixMatch algorithm, capable of handling the interplay between weak labels and residual-based representations. On the benchmark dataset MVTec-AD, our method achieves an Average Precision (AP) of $83.0\%$, surpassing the previous best result of $82.7\%$ in the unsupervised setting. In the supervised AD setting, WeakREST attains an AP of $87.6\%$, outperforming the previous best of $86.0\%$. Notably, even when using weaker annotations such as bounding boxes, WeakREST exceeds the performance of leading methods relying on pixel-wise supervision, achieving an AP of $87.1\%$ compared to the prior best of $86.0\%$ on MVTec-AD.